Summary of Can Large Language Models Understand Uncommon Meanings Of Common Words?, by Jinyang Wu et al.
Can large language models understand uncommon meanings of common words?
by Jinyang Wu, Feihu Che, Xinxin Zheng, Shuai Zhang, Ruihan Jin, Shuai Nie, Pengpeng Shao, Jianhua Tao
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models like ChatGPT have made significant progress in natural language understanding tasks, including dialogue and autonomous agents. However, it remains unclear whether these models genuinely comprehend the world or are just “stochastic parrots.” Most research focuses on surface-level NLU, neglecting fine-grained explorations that can reveal their unique comprehension mechanisms. Our study delves into large language models’ nuanced semantic comprehension capabilities, particularly regarding common words with uncommon meanings. We present an innovative Lexical Semantic Comprehension (LeSC) dataset and evaluation metrics, the first benchmark encompassing both fine-grained and cross-lingual dimensions. Experimental results demonstrate the inferior performance of existing models in this basic lexical-meaning understanding task, even state-of-the-art LLMs like GPT-4 and GPT-3.5 lag behind 16-year-old humans by 3.9% and 22.3%, respectively. Advanced prompting techniques and retrieval-augmented generation are also introduced to help alleviate this trouble, yet limitations persist. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have gotten very good at understanding natural language, but we don’t really know if they truly understand the world or just pretend to. Most research focuses on how well these models do simple tasks like chatting and playing games. But what about more complex things, like understanding the meanings of words? Our study tries to figure this out by looking at how well large language models can understand common words with uncommon meanings. We created a special dataset and ways to measure how good they are, and we found that even really advanced models aren’t very good at this task. |
Keywords
» Artificial intelligence » Gpt » Language understanding » Prompting » Retrieval augmented generation
